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Open Access
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Om Krishna Hankare, Ravikishor Bandla, Gangadri, Navaneeth Harivikas, Mahesh Sutar
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- Student, Student, Student, Student, Professor, Department of CSE (AI & ML), Vishwaniketan’s IMEET, Khalapur Mumbai University, Mumbai, Department of CSE (AI & ML), Vishwaniketan’s IMEET, Khalapur Mumbai University, Mumbai, Department of CSE (AI & ML), Vishwaniketan’s IMEET, Khalapur Mumbai University, Mumbai, Department of CSE (AI & ML), Vishwaniketan’s IMEET, Khalapur Mumbai University, Mumbai, Department of CSE (AI & ML), Vishwaniketan’s IMEET, Khalapur Mumbai University, Mumbai,, Maharashtra, Maharashtra, Maharashtra, Maharashtra, Maharashtra, India, India, India, India, India
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Abstract
nSign language recognition has attracted considerable interest because of its ability to facilitate communication between the deaf community and the public, thereby bridging communication divides. Traditional approaches to sign language recognition often face challenges in accurately interpreting the complex and nuanced gestures inherent in sign languages. However, recent advancements in deep learning techniques have shown promising results in improving the accuracy and robustness of sign language recognition systems. This paper presents an enhanced sign language recognition system utilizing state-of-the-art deep learning architectures. Convolutional neural networks (CNNs) are utilized to extract spatial characteristics from images of sign language, while recurrent neural networks (RNNs) capture the temporal relationships present in sequences of sign language. The proposed system is trained on large-scale sign language datasets to learn discriminative features and improve generalization performance. Furthermore, to address the challenges posed by variations in lighting conditions, backgrounds, and signer characteristics, data augmentation techniques are employed to enhance the robustness of the model. Additionally, transfer learning is explored to leverage pre-trained models on large-scale visual recognition tasks for improved performance on sign language recognition. Experimental results demonstrate that the proposed approach achieves state-of-the-art performance on benchmark sign language recognition datasets, surpassing previous methods in terms of accuracy and generalization. The system’s effectiveness is validated through extensive evaluation on diverse sign language datasets, showcasing its potential for real-world applications in facilitating communication for the hearing- impaired community.
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Keywords: Sign language recognition, deep learning, convolutional neural networks, recurrent neural networks, data augmentation, transfer learning.
n[if 424 equals=”Regular Issue”][This article belongs to Recent Trends in Programming languages(rtpl)]
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References
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | March 14, 2024 | |
| Accepted | March 20, 2024 | |
| Published | April 4, 2024 |
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